{\rtf1\ansi\uc1\deff0\deflang1024
{\fonttbl{\f0\fnil\fcharset0 Times New Roman;}
{\f1\fnil\fcharset0 Arial;}
{\f2\fnil\fcharset0 Arial;}
{\f3\fnil\fcharset0 Courier New;}
{\f4\fnil\fcharset0 Zapf Chancery;}
{\f5\fnil\fcharset0 STIXGeneral;}
}
{\colortbl;
\red0\green0\blue0;
\red0\green0\blue255;
\red0\green255\blue255;
\red0\green255\blue0;
\red255\green0\blue255;
\red255\green0\blue0;
\red255\green255\blue0;
\red255\green255\blue255;
\red0\green0\blue128;
\red0\green128\blue128;
\red0\green128\blue0;
\red128\green0\blue128;
\red128\green0\blue0;
\red128\green128\blue0;
\red128\green128\blue128;
\red192\green192\blue192;
\red239\green219\blue197;
\red205\green149\blue117;
\red253\green217\blue181;
\red120\green219\blue226;
\red135\green169\blue107;
\red255\green164\blue116;
\red250\green231\blue181;
\red159\green129\blue112;
\red253\green124\blue110;
\red35\green35\blue35;
\red31\green117\blue254;
\red173\green173\blue214;
\red25\green158\blue189;
\red115\green102\blue189;
\red222\green93\blue131;
\red203\green65\blue84;
\red180\green103\blue77;
\red255\green127\blue73;
\red234\green126\blue93;
\red176\green183\blue198;
\red255\green255\blue153;
\red28\green211\blue162;
\red255\green170\blue204;
\red221\green68\blue146;
\red29\green172\blue214;
\red188\green93\blue88;
\red221\green148\blue117;
\red154\green206\blue235;
\red255\green188\blue217;
\red253\green219\blue109;
\red43\green108\blue196;
\red239\green205\blue184;
\red110\green81\blue96;
\red29\green249\blue20;
\red113\green188\blue120;
\red109\green174\blue129;
\red195\green100\blue197;
\red204\green102\blue102;
\red231\green198\blue151;
\red252\green217\blue117;
\red168\green228\blue160;
\red149\green145\blue140;
\red28\green172\blue120;
\red240\green232\blue145;
\red255\green29\blue206;
\red178\green236\blue93;
\red93\green118\blue203;
\red202\green55\blue103;
\red59\green176\blue143;
\red253\green252\blue116;
\red252\green180\blue213;
\red255\green189\blue136;
\red246\green100\blue175;
\red205\green74\blue74;
\red151\green154\blue170;
\red255\green130\blue67;
\red200\green56\blue90;
\red239\green152\blue170;
\red253\green188\blue180;
\red26\green72\blue118;
\red48\green186\blue143;
\red25\green116\blue210;
\red255\green163\blue67;
\red186\green184\blue108;
\red255\green117\blue56;
\red230\green168\blue215;
\red65\green74\blue76;
\red255\green110\blue74;
\red28\green169\blue201;
\red255\green207\blue171;
\red197\green208\blue230;
\red253\green215\blue228;
\red21\green128\blue120;
\red252\green116\blue253;
\red247\green128\blue161;
\red142\green69\blue133;
\red116\green66\blue200;
\red157\green129\blue186;
\red255\green29\blue206;
\red255\green73\blue108;
\red214\green138\blue89;
\red255\green72\blue208;
\red227\green37\blue107;
\red238\green32\blue77;
\red255\green83\blue73;
\red192\green68\blue143;
\red31\green206\blue203;
\red120\green81\blue169;
\red255\green155\blue170;
\red252\green40\blue71;
\red118\green255\blue122;
\red159\green226\blue191;
\red165\green105\blue79;
\red138\green121\blue93;
\red69\green206\blue162;
\red251\green126\blue253;
\red205\green197\blue194;
\red128\green218\blue235;
\red236\green234\blue190;
\red255\green207\blue72;
\red253\green94\blue83;
\red250\green167\blue108;
\red252\green137\blue172;
\red219\green215\blue210;
\red23\green128\blue109;
\red222\green170\blue136;
\red119\green221\blue231;
\red253\green252\blue116;
\red146\green110\blue174;
\red247\green83\blue148;
\red255\green160\blue137;
\red143\green80\blue157;
\red237\green237\blue237;
\red162\green173\blue208;
\red255\green67\blue164;
\red252\green108\blue133;
\red205\green164\blue222;
\red252\green232\blue131;
\red197\green227\blue132;
\red255\green182\blue83;
}
{\stylesheet
{\s0\qj\widctlpar\f0\fs20 \snext0 Normal;}
{\cs10 \additive\ssemihidden Default Paragraph Font;}
{\s1\qc\sb240\sa120\keepn\f0\b\fs40 \sbasedon0\snext0 Part;}
{\s2\ql\sb240\sa120\keepn\f0\b\fs40 \sbasedon0\snext0 heading 1;}
{\s3\ql\sb240\sa120\keepn\f0\b\fs32 \sbasedon0\snext0 heading 2;}
{\s4\ql\sb240\sa120\keepn\f0\b\fs32 \sbasedon0\snext0 heading 3;}
{\s5\ql\sb240\sa120\keepn\f0\b\fs24 \sbasedon0\snext0 heading 4;}
{\s6\ql\sb240\sa120\keepn\f0\b\fs24 \sbasedon0\snext0 heading 5;}
{\s7\ql\sb240\sa120\keepn\f0\b\fs24 \sbasedon0\snext0 heading 6;}
{\s8\qr\sb120\sa120\keep\widctlpar\f0 \sbasedon0\snext8 rightpar;}
{\s9\qc\sb120\sa120\keep\widctlpar\f0 \sbasedon0\snext9 centerpar;}
{\s10\ql\sb120\sa120\keep\widctlpar\f0 \sbasedon0\snext10 leftpar;}
{\s11\ql\sb120\sa120\keep\widctlpar\f0 \sbasedon0\snext0 equation;}
{\s12\ql\sb120\sa120\keep\widctlpar\f0 \sbasedon0\snext0 equationNum;}
{\s13\ql\sb120\sa120\keep\widctlpar\f0 \sbasedon0\snext0 equationAlign;}
{\s14\ql\sb120\sa120\keep\widctlpar\f0 \sbasedon0\snext0 equationAlignNum;}
{\s15\ql\sb120\sa120\keep\widctlpar\f0 \sbasedon0\snext0 equationArray;}
{\s16\ql\sb120\sa120\keep\widctlpar\f0 \sbasedon0\snext0 equationArrayNum;}
{\s17\ql\sb120\sa120\keep\widctlpar\f0\fs20 \sbasedon0\snext0 theorem;}
{\s18\ql\sb120\sa120\keep\widctlpar\f0 \sbasedon0\snext0 bitmapCenter;}
{\s20\qc\sb240\sa240\b\f0\fs36 \sbasedon0\snext21 Title;}
{\s21\qc\sa120\f0\fs20 \sbasedon0\snext0 author;}
{\s22\ql\tqc\tx4536\tqr\tx9072\f0\fs20 \sbasedon0\snext22 footer;}
{\s23\ql\tqc\tx4536\tqr\tx9072\f0\fs20 \sbasedon0\snext23 header;}
{\s30\ql\sb120\sa120\keep\widctlpar\f0 \sbasedon0\snext0 caption;}
{\s31\qc\sb120\sa0\keep\widctlpar\f0\fs20 \sbasedon0\snext0 Figure;}
{\s32\qc\sb120\sa0\keep\widctlpar\f0\fs20 \sbasedon0\snext32 Table;}
{\s33\qc\sb120\sa0\keep\widctlpar\f0\fs20 \sbasedon0\snext33 Tabular;}
{\s34\qc\sb120\sa0\keep\widctlpar\f0\fs20 \sbasedon0\snext34 Tabbing;}
{\s35\qj\li1024\ri1024\fi340\widctlpar\f0\fs20 \sbasedon0\snext35 Quote;}
{\s38\ql\widctlpar\f3\fs20 \snext38 verbatim;}
{\s46\ql\fi-283\li283\lin283\sb0\sa120\widctlpar\tql\tx283\f0\fs20 \sbasedon0\snext46 List;}
{\s47\ql\fi-283\li283\lin283\sb0\sa120\widctlpar\tql\tx283\f0\fs20 \sbasedon0\snext47 List 1;}
{\s50\qc\sb120\sa120\keep\widctlpar\f0 \sbasedon0\snext0 latex picture;}
{\s51\qc\sb120\sa120\keep\widctlpar\f0 \sbasedon0\snext0 subfigure;}
{\s61\ql\sb240\sa120\keepn\f0\b\fs32 \sbasedon0\snext62 bibheading;}
{\s62\ql\fi-567\li567\sb0\sa0\f0\fs20 \sbasedon0\snext62 bibitem;}
{\s64\ql\fi-283\li283\lin283\sb0\sa120\widctlpar\tql\tx283\f0\fs20 \sbasedon0\snext64 endnotes;}
{\s65\ql\fi-113\li397\lin397\f0\fs20 \sbasedon0\snext65 footnote text;}
{\s66\qj\fi-170\li454\lin454\f0\fs20 \sbasedon0\snext66 endnote text;}
{\cs62\super \additive\sbasedon10 footnote reference;}
{\cs63\super \additive\sbasedon10 endnote reference;}
{\s67\ql\sb60\sa60\keepn\f0\fs20 \sbasedon0\snext67 acronym;}
{\s70\qc\sa120\b\f0\fs20 \sbasedon0\snext71 abstract title;}
{\s71\qj\li1024\ri1024\fi340\widctlpar\f0\fs20 \sbasedon0\snext0 abstract;}
{\s80\ql\sb240\sa120\keepn\f0\b\fs20 \sbasedon0\snext0 contents_heading;}
{\s81\ql\li425\tqr\tldot\tx8222\sb240\sa60\keepn\f0\fs20\b \sbasedon0\snext82 toc 1;}
{\s82\ql\li512\tqr\tldot\tx8222\sb60\sa60\keepn\f0\fs20 \sbasedon0\snext83 toc 2;}
{\s83\ql\li1024\tqr\tldot\tx8222\sb60\sa60\keepn\f0\fs20 \sbasedon0\snext84 toc 3;}
{\s84\ql\li1536\tqr\tldot\tx8222\sb60\sa60\keepn\f0\fs20 \sbasedon0\snext85 toc 4;}
{\s85\ql\li2048\tqr\tldot\tx8222\sb60\sa60\keepn\f0\fs20 \sbasedon0\snext86 toc 5;}
{\s86\ql\li2560\tqr\tldot\tx8222\sb60\sa60\keepn\f0\fs20 \sbasedon0\snext86 toc 6;}
}
{\info
{\title Original file was SERIMI-MajorRevisionResponse.tex}
{\doccomm Created using latex2rtf 2.3.3 r1230 (released Feb 26, 2013) on Thu Aug 15 11:19:03 2013
}
}
{\footer\pard\plain\f0\fs20\qc\chpgn\par}
\paperw12280\paperh15900\margl2680\margr2700\margt2540\margb1760\pgnstart0\widowctrl\qj\ftnbj\f0\aftnnar
{\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi0 { \par
}\pard\plain\s20\qc\sb240\sa240\b\f0\fs36\sl240\slmult1 \fi300 SERIMI: Class-based Matching for Instance Matching Across Heterogeneous Datasets\par
\pard\plain\s21\qc\sa120\f0\fs20\sl240\slmult1 \fi300 Samur Araujo, Duc Thanh Tran, Arjen P. de Vries and Daniel Schwabe \par
\pard\plain\s21\qc\sa120\f0\fs20\sl240\slmult1 \fi300 \chdate \par
\pard\plain\s3\ql\sb240\sa120\keepn\f0\b\fs32\sl240\slmult1 \sb240 \fi0 1  Responses to All Reviewers\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \sb60 \fi0 First of all, we thank all reviewers for their valuable comments!  We tried to take them all into account to improve the paper. We immediately acknowledge all the suggestions and we have fixed them. The other issues are answered below. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 We restructured the introduction to make examples more clear. Also we restructured section 2 (Overview of the Approach) and Section 3 (Class-Based Matching) into 4 different sections aiming to clarify the definitions, the approach, the class-based matching problem and the proposed solution. The 4 Sections are: \par
{\pard\plain\s46\ql\fi-283\li283\lin283\sb0\sa120\widctlpar\tql\tx283\f0\fs20\sl240\slmult1 \sb50 \li600\fi-300 \bullet\tab
2 Preliminary Definitions \par
\pard\plain\s46\ql\fi-283\li283\lin283\sb0\sa120\widctlpar\tql\tx283\f0\fs20\sl240\slmult1 \sb50 \li600\fi-300 \bullet\tab
3 Overview of The Approach \par
\pard\plain\s46\ql\fi-283\li283\lin283\sb0\sa120\widctlpar\tql\tx283\f0\fs20\sl240\slmult1 \sb50 \li600\fi-300 \bullet\tab
4 Class-Based Matching: The Problem \par
\pard\plain\s46\ql\fi-283\li283\lin283\sb0\sa120\widctlpar\tql\tx283\f0\fs20\sl240\slmult1 \sb50 \li600\fi-300 \bullet\tab
5 Class-Based Matching: A Solution\par
}\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \sb100 \fi300 Other modifications added into the paper to address a particular reviewer\rquote s question will be mentioned on the reviewer\rquote s response.\par
\pard\plain\s3\ql\sb240\sa120\keepn\f0\b\fs32\sl240\slmult1 \sb240 \fi0 2  Responses to Reviewer 1\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \sb60 \fi0 {\b Issue: }{\i The db:Belmount_France example doesn\u226?\u8364?\u8482?t help me understand either. Why is it related to "class-based matching"?  Is it in a heterogeneous setting?  What\rquote s the drawback of the direct matching methods when dealing with it?  }\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } We have restructured the introduction where we made clear the benefits of class-based matching on the proposed example. We also made clear why the direct-matching does not work in the same example. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: }{\i 3. p3, col 1, line 15: "share the same value" Some direct matching solutions do not require this. E.g., [3] deals with edit distance and their candidates do not necessarily share a common attribute. Is it a must for the candidates of your approach?  How do you generate candidates (cf. the comment below on experiment setup)? }\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } We would like to emphasize that the candidates labels should be similar to the source labels, not necessarily the same. In the evaluations, to generate the candidates, we used a simple Boolean query over the tokens of candidate labels, where the tokens of the source labels where the keywords in the queries. Standard data processing was used (e.g. to make tokens lowercase) and stop words were removed (e.g. the, an, a, etc.). These Boolean queries avoid the tuning of thresholds required by more sophisticated direct-matching strategies. We have modified the text to clarify this point.\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: }{\i 4. p5, col 2, line 2: The authors claimed that the common features are deemed to be more characteristic for the class in order to decide whether an instance belong to a class or not. The common features may be in D(X), O(X), and T(X), but only A(X) is composed of class-related features. So I can\rquote t quite agree with the claim. }\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } A definition of a class was added in Sec. 2 to outline the concept of class used in the paper. A class should be understood as a set of instances where each instance in this set must share at least one feature in common to any other instance in this set. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 Taking this definition into account, for class-based matching, any feature is relevant because we cannot assume that the instances (in the heterogeneous setting) will share any class-related feature ({{\i A}({\u8226*})}). For example, in a heterogeneous dataset, two distinct instances in the same class may have different predicates with the same semantics. One may have the predicate {\b0\i0\scaps0\f3 locatedIn} with value "UK" and another the predicate {\b0\i0\scaps0\f3 placedIn} with value "UK". In this case, {{\i A}({\u8226*})} features does not define their syntax similarity (our approach only consider the syntactical similarities), but still we can consider they are similar based only on the value "UK". Especially in heterogeneous data, this assumption works well because instances of a class may not necessarily share the same predicates but at least their values. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 In addition, one purpose of class-based matching is to find a set of instances (among all candidates) that form a concise class, i.e. where the similarity (w.r.t. to {{\i F}({\u8226*})}) of its constituent instances is maximized. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 Therefore, the common features that an instance may share to a class are more important to decide whether it is member of the class or not, than its differences. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: }{\i  I can\rquote t find the implicit class semantics in the equation Eq. 7. Please discuss, maybe with an example showing what class we can infer with the equation.}\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } To clarify the reviewer question, we would like to emphasize that {{\i F}{\i S}{\i S}{\i i}{\i m}} is a simple set similarity function tailored towards the commonalities. It does not aim to capture any class semantics. In the paper, it is discussed the relation between commonalities and differences w.r.t compute a similarity between a two set of features. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: }{\i 5. Sec 3.1, Eq. 7: In the "otherwise" case, the two terms are in different units. The first term {|{\i f}1{\u8745*}{\i f}2|} is a count, while the second term {{\field{\*\fldinst{ EQ  \\F(|{\i f}1\u8722?{\i f}2|+|{\i f}2\u8722?{\i f}1|,2|{\i f}1{\f5\u8746*}{\i f}2|)}}{\fldrslt }}
} is a ratio. I doubt it is mathematically reasonable to subtract one from another, though the scoring function was designed to reflect the bias on commonality.}\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } The first term {|{\i f}1{\u8745*}{\i f}2|} capture how similar two set of instances are (represented by their set of features), while the second term {{\field{\*\fldinst{ EQ  \\F(|{\i f}1\u8722?{\i f}2|+|{\i f}2\u8722?{\i f}1|,2|{\i f}1{\f5\u8746*}{\i f}2|)}}{\fldrslt }}
} captures how different they are. We propose the ratio because we want to score higher an instance {{\i t}} that has the most commonalities to a target set {{\i C}({\i s})} (according to the first term), independently of its differences. However, we still want to distinguish two instances {{\field{\*\fldinst{ EQ {\i t}_{\i i}}}{\fldrslt }}
} and {{\field{\*\fldinst{ EQ {\i t}_{\i j}}}{\fldrslt }}
} that are equally common to the {{\i C}({\i s})} but one is the most different. So, this is the reason the second term is subtracted in the equation. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 In other words, given two candidates {{\field{\*\fldinst{ EQ {\i t}\\s\\do5({\fs16 {\i i}})}}{\fldrslt }}
} and {{\field{\*\fldinst{ EQ {\i t}\\s\\do5({\fs16 {\i j}})}}{\fldrslt }}
}, with {{\i n}} and {{\i n}\u8722?1} features in common to {{\i C}({\i s})}, respectively; as the second term is in the interval [0, 1), {{\field{\*\fldinst{ EQ {\i t}\\s\\do5({\fs16 {\i i}})}}{\fldrslt }}
} will be always more similar to {{\i C}({\i s})} than {{\field{\*\fldinst{ EQ {\i t}\\s\\do5({\fs16 {\i j}})}}{\fldrslt }}
}, independently of their differences to {{\i C}({\i s})}. This equation just computes a score representing this notion of similarity. Observe that empirically, it produces a gain in performance of 8%, compare to Jaccard that implements a unbiased notion of set similarity.\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: }{\i 6. Sec 3.1, Eq. 8: What\rquote s the meaning of the similarity between a candidate (t) and a set of candidates (C(s\rquote ))?  I can\rquote t quite understand why t becomes a correct match for s, if t has the highest similarity to "other candidates of other instances" rather than s itself. Please discuss the rationale. }\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } Notice, we do not compare {{\i t}} with {{\i s}} directly because we are assuming they do not share any features in common. This is the assumption of class-based matching. A hybrid approach combining class-based matching and direct-matching can be considered to reinforce the cases where {{\i s}} and {{\i t}} overlaps. We study this case in the evaluations (S+SR+DM).\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 Also, we assume that matching is performed for a class of source instances {{\i S}}. That is, all {{\i s}{\f5\u8712*}{\i S}} belongs to a specific class. Because {{\i S}} is a class, we assume that the correct matches for {{\i s}{\f5\u8712*}{\i S}} should also belong to a class, i.e., the correct matches should share some common features among themselves. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 Departing from these assumptions, the only information that can be used to find correct matches to {{\i s}} are the candidate instances of all instances {{\i s}{\f5\u8712*}{\i S}}. Class-based matching tries to determine the correct match to {{\i s}} based on the target candidate instances of {{\i s}} and the other candidate sets of the other source instances {{\i S}\u8722?\{{\i s}\}}. Consequently, when we compare {{\i t}} with a set of candidates {{\i C}({\i s}')}, in fact we are comparing a set of features of {{\i t}} with a union of features of all instances in {{\i C}({\i s}')}. Intuitively, if {{\i F}(\{{\i t}\})} does not share any feature with any features in {{\i F}({\i C}({\i s}'))}, then it is not similar to any instance in this candidate set. If {{\i t}} is not similar to any candidate set {{\i C}({\i s}')}, it cannot form a class with any candidate instance; therefore, based on our assumption, it cannot be a correct match for {{\i s}}. Contrarily, a candidate {{\i t}} that are more similar to other candidates sets are more likely to be form a class to other candidates, and therefore, can be a correct match. The method proposed tries to find the set of these candidates that forms the most concise class, but also includes correct matches for the most of the source instances.\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: }{\i Issue: Sec 3.3, Algorithm 3, Line 15: Shouldn\u226?\u8364?\u8482?t the return of {{\u948*}.{\i m}{\i a}{\i x}} be put at the end of the algorithm? }\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } No, it should not. It is in the correct place. Notice that this algorithm has a recursive call before the return statement. The method can return before reaching the end of the algorithm.\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: }{\i 8. Sec 4: I have two questions regarding the experiment setup: (1) What candidate (C(S), not C(S)*) selection algorithm was used in your experiment?  (2) What direct matching algorithm was used in SERIMI?  }\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } Clarify these points:\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 1) We selected as target candidates, target instances that share any token in common with the tokens in the keys of the source instances. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 2 ) We used an elementary direct matching approach in SERIMI that compares the features of {{\i s}} with {{\i t}} using the {{\i F}{\i S}{\i S}{\i i}{\i m}>{\u948*}} similarity. It means that two instances direct match if they share common features ( D(X), O(X), T(X) and A(X) ). The threshold {{\u948*}} was determined by the method discussed in the paper.\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: } {\i 9. Sec 4.2: The reason why the class-based matching performed poorly on Person11-Person12 (F1 = 0.49/0.47) needs some discussion.}\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } Text included in Sec. 6.2:\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 Particularly, S and S+SR performed poorly in Person11-Person12 (49% and 47%, respectively) because features of the candidate instances (all of them) were very similar (e.g. all contained phone, address and were of the type person). Due to this, the class-based matching produced similar scores to all candidates, which were not sufficiently distinct to distinguish the correct matches from the incorrect ones. In this task, the DM performed better, because there were sufficient overlapping between the source and target instances to identify the correct matches.\par
\pard\plain\s3\ql\sb240\sa120\keepn\f0\b\fs32\sl240\slmult1 \sb240 \fi0 3  Responses to Reviewer 2\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \sb60 \fi0 {\b Issue: }{\i W1. The authors modeled the class-based matching as an optimization problem (Definition 3), but it was unclear for me that why the authors defined this problem in this way. }\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } We do so because, the class-based matching is a particular case of clustering, where the candidates {{\i C}} are separated into the matches {{\i M}} and the non-matches {{\field{\*\fldinst{ EQ {\i M}\\s\\up5({\fs16 \u8722?})}}{\fldrslt }}
}. It resembles a clustering problem because it is an unsupervised approach for separating the data spaces (i.e. the candidates {{\i C}}) but it is substantially different from clustering techniques (e.g. k-means). \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 First, class-based matching is particularly interested in finding the set {{\i M}}. It means that a traditional clustering approaches are not sufficient because even if it could separate {{\i C}} in two clusters, we would still have the problem of deciding which one is {{\i M}} between the two clusters. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 Second, the set {{\i M}({\i s})} of matches for {{\i s}{\f5\u8712*}{\i S}} should not be empty. This adds the additional constraint to the problem, not required in the traditional clustering settings, that {{\f5\u8704*}{\i s}{\f5\u8712*}{\i S}:{\i M}({\i s}){\u8800*}{\u8709*}}. Notice that in verifying this constraint equates in computing {{\i M}({\i s})={\i C}({\i s}){\u8745*}{\i M}}, i.e. it consists of mapping the correct matches to their corresponding source instances. Therefore, if we can find {{\i M}}, we solve the problem of finding {{\i M}({\i S})}, as well. Given {{\i M}} and {{\i C}({\i s}){\f5\u8712*}{\i C}({\i S})}, this particular problem of computing {{\i M}{\u8745*}{\i C}({\i s})} for all {{\i s}{\f5\u8712*}{\i S}} can be achieved in {{\i O}({\i l}{\i n}|{\i M}|)}. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 Considering the class-based matching assumption, we can concentrate in finding a subset {{\i M}}, where the similarity among its constituent instances is maximized. Consequently, this problem can then be formulated as follows:\par
{\pard\plain\s17\ql\sb120\sa120\keep\widctlpar\f0\fs20\sl240\slmult1 \sb60 \fi0 {\b Definition 1 (Class-based Matching)} {\i  To find the solution for the class-based matching problem consists of computing {\par
\pard\plain\s12\ql\sb120\sa120\keep\widctlpar\f0\tqc\tx3450\tqr\tx6900\sl240\slmult1 \fi0 \scaps0\i \tab
{\field{\*\fldinst{ EQ \fs16 {}}{\fldrslt }}
\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi0 \scaps0\i \qc [Sorry. Ignored {\plain\f3\\begin\{aligned\} ... \\end\{aligned\}}]\par
}{\*\bkmkstart BMeq_opt}{\*\bkmkend BMeq_opt}\tab{\b0 (\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi0 \scaps0\i 1)}\par
}\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 \scaps0\i where {{\b M}} is the set containing all possible {{\i M}} as elements. The term {{\i S}{\i i}{\i m}({\i t},{\i M})>{\u948*}} captures the heuristic that avoids non-matches, i.e. focuses only on finding {{\i M}}. Precisely, {{\i S}{\i i}{\i m}({\i t},{\i M})} is an arbitrary function (e.g., Jaccard) that returns the similarity between an instance {{\i t}} and an instance-based class representation {{\i M}} and {{\u948*}} is a similarity threshold. Note that {{\i S}{\i i}{\i m}({\i t},{\i M})} operates over {\i features} extracted from the instance {{\i t}} and instances in the sets in {{\i M}}. This will be detail further, in our proposed solution to this problem.\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 \scaps0\i To consider the cases where some candidate set {{\i C}({\i s})} may not contain a match to {{\i s}{\f5\u8712*}{\i S}}, the constrain {{\f5\u8704*}{\i s}{\f5\u8712*}{\i S}:{\i C}({\i s}){\u8745*}{\i M}{\u8800*}{\u8709*}} can be removed and the term\par
{\pard\plain\s12\ql\sb120\sa120\keep\widctlpar\f0\tqc\tx3450\tqr\tx6900\sl240\slmult1 \fi0 \scaps0\i \tab
{\field{\*\fldinst{ EQ {\i Z}= \\F( \\F( \\i \\su({\i s}{\f5\u8712*}{\i S},, )|{\i C}({\i s}){\u8745*}{\i M}|,|{\i C}({\i s})|),|{\i S}|)}}{\fldrslt }}
\tab{\b0 (2)}\par
}\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 \scaps0\i can be added to the Eq. {\field{\*\fldinst{\lang1024 REF BMeq_opt \\* MERGEFORMAT }}{\fldrslt{1}}}. }\par
}\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \sb60 \fi300 Note {{\i Z}} is simply an auxiliary term introduced to deal with the general case where {{\i M}({\i s})={\i C}({\i s}){\u8745*}{\i M}} might be empty. It helps to score higher a solution set {{\i M}{\f5\u8712*}{\b M}} where the majority of {{\i M}({\i s})} has cardinality higher than zero; therefore, avoiding solution sets with many empty matches. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 Intuitively, the idea is to find a solution {{\field{\*\fldinst{ EQ {\i M}\\s\\up5({\fs16 *}){\f5\u8712*}{\b M}}}{\fldrslt }}
}, which contains at least one candidate for every source instance {{\i s}} (considering {|{\i M}({\i s})|>0} in Eq. {\field{\*\fldinst{\lang1024 REF BMeq_opt \\* MERGEFORMAT }}{\fldrslt{1}}}). Comparing to all the other candidate solutions in {{\b M}}, {{\field{\*\fldinst{ EQ {\i M}\\s\\up5({\fs16 *})}}{\fldrslt }}
} is the most similar to the instances it consists of (c.f. {{\i S}{\i i}{\i m}({\i t},{\i M})} in Eq. {\field{\*\fldinst{\lang1024 REF BMeq_opt \\* MERGEFORMAT }}{\fldrslt{1}}}). That is, {{\field{\*\fldinst{ EQ {\i M}\\s\\up5({\fs16 *})}}{\fldrslt }}
} is not only the result but at the same time, acts as the class that is compared with the candidates instances {{\i t}{\f5\u8712*}{\i C}}. \par
{\i \pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 \scaps0\i W2. Since the optimal solution cannot be achieved for class-based matching, the authors proposed several heuristic approaches to derive an approximate solution, such as replacing M(S) with C(S), and using a greedy algorithm to derive the local optimal solution. But the authors did not investigate that how such approximations will affect the result quality. Please add some theoretical and experimental analysis of this part. }\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } {\cf6 @TODO: I am working on a solution.}\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: }{\i W4. The authors conducted extensive experiments to evaluate the running time and the accuracy of their approaches. In terms of the accuracy, the authors mainly reported the F1 values. Why not show the precision/recall results?  I believe that will help readers to fully understand that why the class-based matching can improve the result accuracy (is it due to the improvement of precision, or recall, or both? ).}\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } {\cf6 @TODO: Tran, should I add these tables? }\par
\pard\plain\s3\ql\sb240\sa120\keepn\f0\b\fs32\sl240\slmult1 \sb240 \fi0 4  Responses to Reviewer 3\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \sb60 \fi0 {\b Issue: }1, {\i The aim of this paper is to calculate M*(S). As the direct enumerate M*(S) is expensive. The author use a heuristic methods to approximately calculate M(S). The whole method dose not have any analysis on why this heuristic works and dose not included any theoretical analysis on how good this approximation is. Please give more deep analysis on this part.}\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. }This is exactly the same question than the reviewer 1. Any suggestion how to approach it? \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: }{\i 2, The feature set introduced in 3.1 is using all parts of the sets of construct a feature set. The feature set includes predicates, instance, and literals. Those features are treated equal. Should the literal are more important than predicate? }\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } To avoid biasing the solution towards a specific feature set, we considered all features ({{\i A}({\u8901*})}, {{\i D}({\u8901*})}, {{\i O}({\u8901*})} and {{\i T}({\u8901*})}) equally determinant in our setting. As we observed empirically, on average, this strategy produced better results than the settings where we removed any feature set. We acknowledge that a fine-grained weighting of the features may improve the method; however, this requires a non-trivial solution, potentially requiring training data, to be consider as future research. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: }{\i 3, The similarity function FSSim is biased. The difference of two sets only amount for 1 unit of score, why this biased similarity function works?  Is this similarity function has any knowledge to support.}\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } The first term {|{\i f}1{\u8745*}{\i f}2|} capture how similar two set of instances are (represented by their set of features), while the second term {{\field{\*\fldinst{ EQ  \\F(|{\i f}1\u8722?{\i f}2|+|{\i f}2\u8722?{\i f}1|,2|{\i f}1{\f5\u8746*}{\i f}2|)}}{\fldrslt }}
} captures how different they are. We propose this function because we want to score higher an instance {{\i t}} that has the most commonalities to a target set {{\i C}({\i s})} (according to the first term), independently of its differences. However, we still want to distinguish two instances {{\field{\*\fldinst{ EQ {\i t}_{\i i}}}{\fldrslt }}
} and {{\field{\*\fldinst{ EQ {\i t}_{\i j}}}{\fldrslt }}
} that are equally common to the {{\i C}({\i s})} but one is the most different. So, this is the reason the second term is subtracted in the equation. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 In other words, given two candidates {{\field{\*\fldinst{ EQ {\i t}\\s\\do5({\fs16 {\i i}})}}{\fldrslt }}
} and {{\field{\*\fldinst{ EQ {\i t}\\s\\do5({\fs16 {\i j}})}}{\fldrslt }}
}, with {{\i n}} and {{\i n}\u8722?1} features in common to {{\i C}({\i s})}, respectively; as the second term is in the interval [0, 1), {{\field{\*\fldinst{ EQ {\i t}\\s\\do5({\fs16 {\i i}})}}{\fldrslt }}
} will be always more similar to {{\i C}({\i s})} than {{\field{\*\fldinst{ EQ {\i t}\\s\\do5({\fs16 {\i j}})}}{\fldrslt }}
}, independently of their differences to {{\i C}({\i s})}. This equation just computes a score representing this notion of similarity. \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 An intuition why it works is because class-based matching tries to determine the correct match to {{\i s}} based on the target candidate instances of {{\i s}} and the other candidate sets of the other source instances {{\i S}\u8722?\{{\i s}\}}. Consequently, when we compare {{\i t}} with a set of candidates {{\i C}({\i s}')}, in fact we are comparing a set of features of {{\i t}} with a union of features of all instances in {{\i C}({\i s}')}. Intuitively, if {{\i F}(\{{\i t}\})} does not share any feature with any features in {{\i F}({\i C}({\i s}'))}, then it is not similar to any instance in this candidate set. If {{\i t}} is not similar to any candidate set {{\i C}({\i s}')}, it cannot form a class with any candidate instance; therefore, based on our assumption, it cannot be a correct match for {{\i s}}. Contrarily, a candidate {{\i t}} that are more similar to other candidates sets are more likely to be form a class to other candidates, and therefore, can be a correct match. As the difference between {{\i F}(\{{\i t}\})} and {{\i F}({\i C}({\i s}'))} will be always higher than commonalities, the proposed function smoothes the differences to give higher score to the commonalities.\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 Observe that empirically, it produces a gain in performance of 8%, compare to Jaccard that implements a unbiased notion of set similarity.\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: }{\i 4, The score of one instance is calculated by intersect the feature set with the complementary candidate sets\u226?\u8364?\u8482? features. What if the score is high with the complementary set but it is totally dissimilar with the source instance it comes from.}\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } Notice, we do not compare {{\i t}} with {{\i s}} directly because we are assuming they do not share any features in common. This is the assumption of class-based matching. A hybrid approach combining class-based matching and direct-matching can be considered to reinforce the cases where {{\i s}} and {{\i t}} overlaps. We study this case in the evaluations (S+SR+DM).\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: }{\i The matching could result in false positive. Is there any assumption on the source instances that ensure the false positive could be minimized?  Another words, what kind of source instance sets this method is preferred? }\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } Regarding the source instances, class-based matching should be applied to match a class of source instances. Theoretically, the method should perform better when the source instances are specific to one class (e.g. people ), than when the source instances are from many classes (e.g. people and locations). The reason for that is that candidate instances for mixed source instances will potentially carry more noise. Consequently, the accuracy of the method will be impacted by that. Mixed source instances should be first split into sets of specific classes before applying the method. Overall, class-based matching is recommended for cases where the source instances belong to a well-defined class (e.g. people, politicians, countries, locations, cities, etc). \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Issue: } {\i The author should give more introductions on the measurements. For instance, not every ready know what F1 is without reading the related work. } \par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Response. } Text included in Sec. 6:\par
\pard\plain\s0\qj\widctlpar\f0\fs20\sl240\slmult1 \fi300 {\b Evaluation Metrics.} We used the standard metrics precision (proportion of correct matches among matches found), recall (proportion of matches identified among all actual matches) and F1 (harmonic mean between precision and recall) to measure the result accuracy (also employed by OAEI). To compute these metrics, the provided reference mappings were used as the ground truth. \par
{\pard\plain\s12\ql\sb120\sa120\keep\widctlpar\f0\tqc\tx3450\tqr\tx6900\sl240\slmult1 \fi0 \tab
{\field{\*\fldinst{ EQ {\i F}1=2{\u215*} \\F({\i R}{\i e}{\i c}{\i a}{\i l}{\i l}{\u215*}{\i P}{\i r}{\i e}{\i c}{\i i}{\i s}{\i i}{\i o}{\i n},{\i R}{\i e}{\i c}{\i a}{\i l}{\i l}+{\i P}{\i r}{\i e}{\i c}{\i i}{\i s}{\i i}{\i o}{\i n})}}{\fldrslt }}
\tab{\b0 (3)}\par
}}}
